


from typing import List, Optional, Union

import torch
from transformers import (
    XLMRobertaTokenizer,
)

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from .text_encoder import MultilingualCLIP


lowerCAmelCase			=				logging.get_logger(__name__)  # pylint: disable=invalid-name

lowerCAmelCase			=				'\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyPipeline, KandinskyPriorPipeline\n        >>> import torch\n\n        >>> pipe_prior = KandinskyPriorPipeline.from_pretrained("kandinsky-community/Kandinsky-2-1-prior")\n        >>> pipe_prior.to("cuda")\n\n        >>> prompt = "red cat, 4k photo"\n        >>> out = pipe_prior(prompt)\n        >>> image_emb = out.image_embeds\n        >>> negative_image_emb = out.negative_image_embeds\n\n        >>> pipe = KandinskyPipeline.from_pretrained("kandinsky-community/kandinsky-2-1")\n        >>> pipe.to("cuda")\n\n        >>> image = pipe(\n        ...     prompt,\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=negative_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=100,\n        ... ).images\n\n        >>> image[0].save("cat.png")\n        ```\n'
def      _a					( SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE=8	):


		"""simple docstring"""




		lowercase__				= h // scale_factor**2
		if h % scale_factor**2 != 0:
				new_h += 1
		lowercase__				= w // scale_factor**2
		if w % scale_factor**2 != 0:
				new_w += 1
		return new_h * scale_factor, new_w * scale_factor


class 			_a				(     UpperCamelCase__     ):

							def __init__(				self:      str       ,		UpperCamelCase_:      MultilingualCLIP       ,		UpperCamelCase_:      XLMRobertaTokenizer       ,		UpperCamelCase_:      UNetaDConditionModel       ,		UpperCamelCase_:      Union[DDIMScheduler, DDPMScheduler]       ,		UpperCamelCase_:      VQModel       ,		)     ->  str:





									"""simple docstring"""







									super().__init__()

									self.register_modules(
									    text_encoder=UpperCamelCase_       ,		tokenizer=UpperCamelCase_       ,		unet=UpperCamelCase_       ,		scheduler=UpperCamelCase_       ,		movq=UpperCamelCase_       ,		)
									lowercase__				= 2 ** (len(self.movq.config.block_out_channels				) - 1)

							def  lowerCamelCase_					(				self:      Optional[int]       ,		UpperCamelCase_:      int       ,		UpperCamelCase_:      str       ,		UpperCamelCase_:      str       ,		UpperCamelCase_:      Dict       ,		UpperCamelCase_:      str       ,		UpperCamelCase_:      List[str]				)     ->  List[str]:





									"""simple docstring"""







									if latents is None:
											lowercase__				= randn_tensor(UpperCamelCase_       ,		generator=UpperCamelCase_       ,		device=UpperCamelCase_       ,		dtype=UpperCamelCase_				)
									else:
											if latents.shape != shape:
													raise ValueError(f'Unexpected latents shape, got {latents.shape}, expected {shape}'				)
											lowercase__				= latents.to(UpperCamelCase_				)

									lowercase__				= latents * scheduler.init_noise_sigma
									return latents

							def  lowerCamelCase_					(				self:      List[str]       ,		UpperCamelCase_:      Dict       ,		UpperCamelCase_:      Any       ,		UpperCamelCase_:      Any       ,		UpperCamelCase_:      Union[str, Any]       ,		UpperCamelCase_:      Optional[int]=None       ,		)     ->  List[str]:





									"""simple docstring"""







									lowercase__				= len(UpperCamelCase_				) if isinstance(UpperCamelCase_       ,		UpperCamelCase_				) else 1
									# get prompt text embeddings
									lowercase__				= self.tokenizer(
									    UpperCamelCase_       ,		padding='''max_length'''       ,		truncation=UpperCamelCase_       ,		max_length=77       ,		return_attention_mask=UpperCamelCase_       ,		add_special_tokens=UpperCamelCase_       ,		return_tensors='''pt'''       ,		)

									lowercase__				= text_inputs.input_ids
									lowercase__				= self.tokenizer(UpperCamelCase_       ,		padding='''longest'''       ,		return_tensors='''pt'''				).input_ids

									if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(UpperCamelCase_       ,		UpperCamelCase_				):
											lowercase__				= self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]				)
											logger.warning(
											    '''The following part of your input was truncated because CLIP can only handle sequences up to'''
											    f' {self.tokenizer.model_max_length} tokens: {removed_text}'				)

									lowercase__				= text_input_ids.to(UpperCamelCase_				)
									lowercase__				= text_inputs.attention_mask.to(UpperCamelCase_				)

									lowercase__	,						lowercase__				= self.text_encoder(
									    input_ids=UpperCamelCase_       ,		attention_mask=UpperCamelCase_				)

									lowercase__				= prompt_embeds.repeat_interleave(UpperCamelCase_       ,		dim=0				)
									lowercase__				= text_encoder_hidden_states.repeat_interleave(UpperCamelCase_       ,		dim=0				)
									lowercase__				= text_mask.repeat_interleave(UpperCamelCase_       ,		dim=0				)

									if do_classifier_free_guidance:
											lowercase__				= 42
											if negative_prompt is None:
													lowercase__				= [''''''] * batch_size
											elif type(UpperCamelCase_				) is not type(UpperCamelCase_				):
													raise TypeError(
													    f'`negative_prompt` should be the same type to `prompt`, but got {type(UpperCamelCase_				)} !='
													    f' {type(UpperCamelCase_				)}.'				)
											elif isinstance(UpperCamelCase_       ,		UpperCamelCase_				):
													lowercase__				= [negative_prompt]
											elif batch_size != len(UpperCamelCase_				):
													raise ValueError(
													    f'`negative_prompt`: {negative_prompt} has batch size {len(UpperCamelCase_				)}, but `prompt`:'
													    f' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'
													    ''' the batch size of `prompt`.'''				)
											else:
													lowercase__				= negative_prompt

											lowercase__				= self.tokenizer(
											    UpperCamelCase_       ,		padding='''max_length'''       ,		max_length=77       ,		truncation=UpperCamelCase_       ,		return_attention_mask=UpperCamelCase_       ,		add_special_tokens=UpperCamelCase_       ,		return_tensors='''pt'''       ,		)
											lowercase__				= uncond_input.input_ids.to(UpperCamelCase_				)
											lowercase__				= uncond_input.attention_mask.to(UpperCamelCase_				)

											lowercase__	,						lowercase__				= self.text_encoder(
											    input_ids=UpperCamelCase_       ,		attention_mask=UpperCamelCase_				)

											# duplicate unconditional embeddings for each generation per prompt, using mps friendly method

											lowercase__				= negative_prompt_embeds.shape[1]
											lowercase__				= negative_prompt_embeds.repeat(1       ,		UpperCamelCase_				)
											lowercase__				= negative_prompt_embeds.view(batch_size * num_images_per_prompt       ,		UpperCamelCase_				)

											lowercase__				= uncond_text_encoder_hidden_states.shape[1]
											lowercase__				= uncond_text_encoder_hidden_states.repeat(1       ,		UpperCamelCase_       ,		1				)
											lowercase__				= uncond_text_encoder_hidden_states.view(
											    batch_size * num_images_per_prompt       ,		UpperCamelCase_       ,		-1				)
											lowercase__				= uncond_text_mask.repeat_interleave(UpperCamelCase_       ,		dim=0				)

											# done duplicates

											# For classifier free guidance, we need to do two forward passes.
											# Here we concatenate the unconditional and text embeddings into a single batch
											# to avoid doing two forward passes
											lowercase__				= torch.cat([negative_prompt_embeds, prompt_embeds]				)
											lowercase__				= torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]				)

											lowercase__				= torch.cat([uncond_text_mask, text_mask]				)

									return prompt_embeds, text_encoder_hidden_states, text_mask

							def  lowerCamelCase_					(				self:      Tuple       ,		UpperCamelCase_:      int=0				)     ->  List[str]:





									"""simple docstring"""







									if is_accelerate_available():
											from accelerate import cpu_offload
									else:
											raise ImportError('''Please install accelerate via `pip install accelerate`'''				)

									lowercase__				= torch.device(f'cuda:{gpu_id}'				)

									lowercase__				= [
									    self.unet,
									    self.text_encoder,
									    self.movq,
									]
									for cpu_offloaded_model in models:
											if cpu_offloaded_model is not None:
													cpu_offload(UpperCamelCase_       ,		UpperCamelCase_				)

							def  lowerCamelCase_					(				self:      Tuple       ,		UpperCamelCase_:      int=0				)     ->  str:





									"""simple docstring"""







									if is_accelerate_available() and is_accelerate_version('''>='''       ,		'''0.17.0.dev0'''				):
											from accelerate import cpu_offload_with_hook
									else:
											raise ImportError('''`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'''				)

									lowercase__				= torch.device(f'cuda:{gpu_id}'				)

									if self.device.type != "cpu":
											self.to('''cpu'''       ,		silence_dtype_warnings=UpperCamelCase_				)
											torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

									lowercase__				= None
									for cpu_offloaded_model in [self.text_encoder, self.unet, self.movq]:
											lowercase__	,						lowercase__				= cpu_offload_with_hook(UpperCamelCase_       ,		UpperCamelCase_       ,		prev_module_hook=UpperCamelCase_				)

									if self.safety_checker is not None:
											lowercase__	,						lowercase__				= cpu_offload_with_hook(self.safety_checker       ,		UpperCamelCase_       ,		prev_module_hook=UpperCamelCase_				)

									# We'll offload the last model manually.
									lowercase__				= hook

							@property
							# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
							def  lowerCamelCase_					(				self:      Tuple				)     ->  Optional[int]:





									"""simple docstring"""







									if not hasattr(self.unet       ,		'''_hf_hook'''				):
											return self.device
									for module in self.unet.modules():
											if (
											    hasattr(UpperCamelCase_       ,		'''_hf_hook'''				)
											    and hasattr(module._hf_hook       ,		'''execution_device'''				)
											    and module._hf_hook.execution_device is not None
											):
													return torch.device(module._hf_hook.execution_device				)
									return self.device


							@torch.no_grad()
							@replace_example_docstring(UpperCamelCase_				)
							def __call__(				self:      Tuple       ,		UpperCamelCase_:      Union[str, List[str]]       ,		UpperCamelCase_:      Union[torch.FloatTensor, List[torch.FloatTensor]]       ,		UpperCamelCase_:      Union[torch.FloatTensor, List[torch.FloatTensor]]       ,		UpperCamelCase_:      Optional[Union[str, List[str]]] = None       ,		UpperCamelCase_:      int = 512       ,		UpperCamelCase_:      int = 512       ,		UpperCamelCase_:      int = 100       ,		UpperCamelCase_:      float = 4.0       ,		UpperCamelCase_:      int = 1       ,		UpperCamelCase_:      Optional[Union[torch.Generator, List[torch.Generator]]] = None       ,		UpperCamelCase_:      Optional[torch.FloatTensor] = None       ,		UpperCamelCase_:      Optional[str] = "pil"       ,		UpperCamelCase_:      bool = True       ,		)     ->  Tuple:





									"""simple docstring"""







									if isinstance(UpperCamelCase_       ,		UpperCamelCase_				):
											lowercase__				= 1
									elif isinstance(UpperCamelCase_       ,		UpperCamelCase_				):
											lowercase__				= len(UpperCamelCase_				)
									else:
											raise ValueError(f'`prompt` has to be of type `str` or `list` but is {type(UpperCamelCase_				)}'				)

									lowercase__				= self._execution_device

									lowercase__				= batch_size * num_images_per_prompt
									lowercase__				= guidance_scale > 1.0

									lowercase__	,						lowercase__	,						lowercase__				= self._encode_prompt(
									    UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_				)

									if isinstance(UpperCamelCase_       ,		UpperCamelCase_				):
											lowercase__				= torch.cat(UpperCamelCase_       ,		dim=0				)
									if isinstance(UpperCamelCase_       ,		UpperCamelCase_				):
											lowercase__				= torch.cat(UpperCamelCase_       ,		dim=0				)

									if do_classifier_free_guidance:
											lowercase__				= image_embeds.repeat_interleave(UpperCamelCase_       ,		dim=0				)
											lowercase__				= negative_image_embeds.repeat_interleave(UpperCamelCase_       ,		dim=0				)

									lowercase__				= torch.cat([negative_image_embeds, image_embeds]       ,		dim=0				).to(
									    dtype=prompt_embeds.dtype       ,		device=UpperCamelCase_				)

									self.scheduler.set_timesteps(UpperCamelCase_       ,		device=UpperCamelCase_				)
									lowercase__				= self.scheduler.timesteps

									lowercase__				= self.unet.config.in_channels

									lowercase__	,						lowercase__				= get_new_h_w(UpperCamelCase_       ,		UpperCamelCase_       ,		self.movq_scale_factor				)

									# create initial latent
									lowercase__				= self.prepare_latents(
									    (batch_size, num_channels_latents, height, width)       ,		text_encoder_hidden_states.dtype       ,		UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_       ,		self.scheduler       ,		)

									for i, t in enumerate(self.progress_bar(UpperCamelCase_				)				):
											# expand the latents if we are doing classifier free guidance
											lowercase__				= torch.cat([latents] * 2				) if do_classifier_free_guidance else latents

											lowercase__				= {'''text_embeds''': prompt_embeds, '''image_embeds''': image_embeds}
											lowercase__				= self.unet(
											    sample=UpperCamelCase_       ,		timestep=UpperCamelCase_       ,		encoder_hidden_states=UpperCamelCase_       ,		added_cond_kwargs=UpperCamelCase_       ,		return_dict=UpperCamelCase_       ,		)[0]

											if do_classifier_free_guidance:
													lowercase__	,						lowercase__				= noise_pred.split(latents.shape[1]       ,		dim=1				)
													lowercase__	,						lowercase__				= noise_pred.chunk(2				)
													lowercase__	,						lowercase__				= variance_pred.chunk(2				)
													lowercase__				= noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
													lowercase__				= torch.cat([noise_pred, variance_pred_text]       ,		dim=1				)

											if not (
											    hasattr(self.scheduler.config       ,		'''variance_type'''				)
											    and self.scheduler.config.variance_type in ["learned", "learned_range"]
											):
													lowercase__	,						lowercase__				= noise_pred.split(latents.shape[1]       ,		dim=1				)

											# compute the previous noisy sample x_t -> x_t-1
											lowercase__				= self.scheduler.step(
											    UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_       ,		generator=UpperCamelCase_       ,		).prev_sample
									# post-processing
									lowercase__				= self.movq.decode(UpperCamelCase_       ,		force_not_quantize=UpperCamelCase_				)['''sample''']

									if output_type not in ["pt", "np", "pil"]:
											raise ValueError(f'Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}'				)

									if output_type in ["np", "pil"]:
											lowercase__				= image * 0.5 + 0.5
											lowercase__				= image.clamp(0       ,		1				)
											lowercase__				= image.cpu().permute(0       ,		2       ,		3       ,		1				).float().numpy()

									if output_type == "pil":
											lowercase__				= self.numpy_to_pil(UpperCamelCase_				)

									if not return_dict:
											return (image,)

									return ImagePipelineOutput(images=UpperCamelCase_				)




import string

def a__       (			A_   ):






  '''simple docstring'''





  __magic_name__								=			""""""
  for i in sequence:
    __magic_name__								=			ord(A_   )
    if 65 <= extract <= 90:
      output += chr(155 - extract   )
    elif 97 <= extract <= 122:
      output += chr(219 - extract   )
    else:
      output += i
  return output

def a__       (			A_   ):






  '''simple docstring'''





  __magic_name__								=			string.ascii_letters
  __magic_name__								=			string.ascii_lowercase[::-1] + string.ascii_uppercase[::-1]
  return "".join(
      letters_reversed[letters.index(A_   )] if c in letters else c for c in sequence   )

def a__       (			):






  '''simple docstring'''





  from timeit import timeit

  print("""Running performance benchmarks..."""   )
  __magic_name__								=			"""from string import printable ; from __main__ import atbash, atbash_slow"""
  print(f'''> atbash_slow(): {timeit('atbash_slow(printable)',	setup=A_   )} seconds'''   )
  print(f'''>      atbash(): {timeit('atbash(printable)',	setup=A_   )} seconds'''   )


if __name__ == "__main__":
    for example in ("ABCDEFGH", "123GGjj", "testStringtest", "with space"):
        print(F'''{example} encrypted in atbash: {atbash(example)}''')
    benchmark()

